Television Rating Control in the Multichannel
Environment Using Trend Fuzzy Knowledge Bases
and Monitoring Results †

The purpose of this study is to control the ratio of programs of different genres whenforming the broadcast grid in order to increase and maintain the rating of a channel. In themultichannel environment, television rating controls consist of selecting content, the ratings ofwhich are completely restored after advertising. The hybrid approach to rule set refinement basedon fuzzy relational calculus simplifies the process of expert recommendation systems construction.By analogy with the problem of the inverted pendulum control, the managerial actions aim to retainthe balance between the fuzzy demand and supply. The increase or decrease trends of the demandand supply are described by primary fuzzy relations. The rule-based solutions of fuzzy relationalequations connect significance measures of the primary fuzzy terms. Program set refinement bysolving fuzzy relational equations allows avoiding procedures of content-based selective filtering.The solution set generation corresponds to the granulation of television time, where each solutionrepresents the time slot and the granulated rating of the content. In automated media planning,generation of the weekly TV program in the form of the granular solution provides the decrease oftime needed for the programming of the channel broadcast grid.

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Television Rating Control in the Multichannel Environment Using Trend Fuzzy Knowledge Bases and Monitoring Results

10.20944/preprints201811.0092.v1 ◽

2018 ◽

Author(s):

Olexiy Azarov ◽

Leonid Krupelnitsky ◽

Hanna Rakytyanska

Keyword(s):

Hybrid Approach ◽

Knowledge Bases ◽

Fuzzy Relational Equations ◽

Media Planning ◽

Demand And Supply ◽

Fuzzy Knowledge Bases ◽

Tv Program ◽

Television Time ◽

Significance Measures ◽

Intensive Procedures

The purpose of the study is to control the ratio of programs of different genres when forming the broadcast grid in order to increase and maintain the rating of the channel. In the multichannel environment, television rating control consists of selecting such content, ratings of which are completely restored after advertising. A hybrid approach combining the benefits of semantic training and fuzzy relational equations in simplification of the expert recommendation systems construction is proposed. The problem of retaining the television rating can be attributed to the problems of fuzzy resources control. The increase or decrease trends of the demand and supply are described by primary fuzzy relations. The rule-based solutions of fuzzy relational equations connect significance measures of the primary fuzzy terms. Rules refinement by solving fuzzy relational equations allows avoiding labor-intensive procedures for the generation and selection of expert rules. The solution set generation corresponds to the granulation of the television time, where each solution represents the time slot and the granulated rating of the content. In automated media planning, generation of the weekly TV program in the form of the granular solutions provides the decrease of the time needed for the programming of the channel broadcast grid.

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Real/Binary-Like Coded Genetic Algorithm to Automatically Generate Fuzzy Knowledge Bases

The Fourth International Conference on Control and Automation 2003 ICCA Final Program and Book of Abstracts ICCA-03 ◽

10.1109/icca.2003.1595133 ◽

2003 ◽

Cited By ~ 1

Author(s):

S. Achiche ◽

L. Baron ◽

M. Balazinski

Keyword(s):

Genetic Algorithm ◽

Knowledge Bases ◽

Fuzzy Knowledge Bases ◽

Fuzzy Knowledge

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The use of fuzzy databases and knowledge bases for aiding engineering projects

Budownictwo i Architektura ◽

10.35784/bud-arch.2175 ◽

2013 ◽

Vol 12 (1) ◽

pp. 069-076

Author(s):

Janusz Szelka ◽

Zbigniew Wrona

Keyword(s):

Decision Making ◽

Database Systems ◽

Knowledge Bases ◽

Fuzzy Data ◽

Fuzzy Knowledge Bases ◽

Decision Making Processes ◽

Engineering Projects ◽

Fuzzy Knowledge

The IT tools that are widely used for aiding information and decision-making tasks in engineering activities include classic database systems, and in the case of problems with poorly-recognised structure – systems with knowledge bases. The uniqueness of these categories of systems allows, however, neither to represent the approximate or imprecise nature of available data or knowledge nor to process fuzzy data. Since so far there have been no solutions related to the use of fuzzy databases or fuzzy knowledge bases in engineering projects, it seems necessary to make an attempt to assess the possible employment of these technologies to aid analytical and decision-making processes.

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Computational Intelligence for Modelling and Control of Multi-Robot Systems

Computational Intelligence in Control ◽

10.4018/978-1-59140-037-0.ch007 ◽

2011 ◽

pp. 122-135

Author(s):

M. Mohammadian

Keyword(s):

Genetic Algorithms ◽

Fuzzy Logic ◽

Control Systems ◽

Fuzzy Systems ◽

Methodological Approach ◽

Knowledge Bases ◽

Fuzzy Logic Systems ◽

Fuzzy Knowledge Bases ◽

Robot Systems ◽

Logic Systems

With increased application of fuzzy logic in complex control systems, there is a need for a structured methodological approach in the development of fuzzy logic systems. Current fuzzy logic systems are developed based on individualistic bases and cannot face the challenge of interacting with other (fuzzy) systems in a dynamic environment. In this chapter a method for development of fuzzy systems that can interact with other (fuzzy) systems is proposed. Specifically a method for designing hierarchical self-learning fuzzy logic control systems based on the integration of genetic algorithms and fuzzy logic to provide an integrated knowledge base for intelligent control of mobile robots for collision-avoidance in a common workspace. The robots are considered as point masses moving in a common work space. Genetic algorithms are employed as an adaptive method for learning the fuzzy rules of the control systems as well as learning, the mapping and interaction between fuzzy knowledge bases of different fuzzy logic systems.

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EGEA : A New Hybrid Approach Towards Extracting Reduced Generic Association Rule Set (Application to AML Blood Cancer Therapy)

Data Warehousing and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/11823728_47 ◽

2006 ◽

pp. 491-502 ◽

Cited By ~ 1

Author(s):

M. A. Esseghir ◽

G. Gasmi ◽

S. Ben Yahia ◽

Y. Slimani

Keyword(s):

Cancer Therapy ◽

Association Rule ◽

Hybrid Approach ◽

Blood Cancer ◽

Rule Set

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A Statistical, Grammar-Based Approach to Microplanning

Computational Linguistics ◽

10.1162/coli_a_00273 ◽

2017 ◽

Vol 43 (1) ◽

pp. 1-30 ◽

Cited By ~ 4

Author(s):

Claire Gardent ◽

Laura Perez-Beltrachini

Keyword(s):

Statistical Approach ◽

Hybrid Approach ◽

Human Study ◽

Knowledge Bases ◽

Symbolic System ◽

Language Generation ◽

Fine Grained ◽

Sentence Segmentation ◽

Symbolic Approach ◽

Surface Realization

Although there has been much work in recent years on data-driven natural language generation, little attention has been paid to the fine-grained interactions that arise during microplanning between aggregation, surface realization, and sentence segmentation. In this article, we propose a hybrid symbolic/statistical approach to jointly model the constraints regulating these interactions. Our approach integrates a small handwritten grammar, a statistical hypertagger, and a surface realization algorithm. It is applied to the verbalization of knowledge base queries and tested on 13 knowledge bases to demonstrate domain independence. We evaluate our approach in several ways. A quantitative analysis shows that the hybrid approach outperforms a purely symbolic approach in terms of both speed and coverage. Results from a human study indicate that users find the output of this hybrid statistic/symbolic system more fluent than both a template-based and a purely symbolic grammar-based approach. Finally, we illustrate by means of examples that our approach can account for various factors impacting aggregation, sentence segmentation, and surface realization.

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